Data Analyst

Atmos Energy•Dallas, TX

About The Position

The Data Analyst executes existing machine learning models in support of daily operations, long-term planning, and forecasting. The role reconciles actual and anticipated outcomes, analyzes discrepancies to isolate issues, and supports continuous improvement. It modifies plans and forecasts when needed, communicates adjustments, and identifies data that could improve model quality and prediction accuracy. The analyst assesses new data sources and collection techniques, applies statistical and analytical methods to identify trends and dependencies, and generates operational insights that may include recommendations. The role supports data integrity and normalization, uses database platforms for data collection and ad hoc or root-cause analysis, and participates in developing reports. It presents information clearly using data visualization tools and techniques and stays current with emerging data tools and technology. The position works in an indoor environment and may occasionally involve bending, stooping, grasping, reaching, twisting, turning, or lifting.

Requirements

Bachelor's degree in MIS, Data Analytics, mathematics, engineering, statistics, or a related field.
At least 1 year of relevant experience.
Strong mathematical background and ability to perform analysis involving statistical methods and regression analysis.
Ability to communicate technical concepts and provide or obtain information from people with limited technical experience or knowledge.
Ability to perform analyses involving ratios, percentages, and simple statistical methods.
Working knowledge of software applications used to create and maintain documents, reports, spreadsheets, and/or graphics.

Nice To Haves

Knowledge of statistical computer languages, data visualization tools, and business intelligence tools.

Responsibilities

Execute existing machine learning models for daily operations, long-term planning, and forecasting.
Reconcile actual and anticipated outcomes, analyze discrepancies by component, and identify issues for continuous improvement.
Modify plans and forecasts as necessary and communicate adjustments to appropriate personnel.
Identify additional data that could improve model quality and prediction accuracy.
Assess the effectiveness and accuracy of new data sources and data-gathering techniques.
Apply statistical and analytical methods to identify trends and dependencies, generate operational insights, and potentially recommend actions.
Support data integrity and normalization initiatives.
Use database platforms to collect data and conduct ad hoc and root-cause analysis of complex issues.
Participate in report development and present information clearly using data visualization tools and techniques.
Stay abreast of emerging and evolving data tools and technology.